This application is a collaborative project between an experimental biologist and a theoretical mathematician in order to develop a simulation framework to model the early stages of salivary gland branching morphogenesis and create an interactive tool that can be used to predict cell behavior within this context. Existing strategies for engineering salivary glands have been unable to create a complex branched structure or successfully produce saliva-secreting acinar cells, which may relate to the lack of appropriate 3D structure in these models. Although there is currently a clinical need for artificial salivary glands to replace the damaged saliva-producing tissue in patients suffering from Sjgren's Syndrome or from side effects of radiation therapy for head and neck tumors, few predictive tools are available to model cell behavior. To engineer branched tissues, we need to understand how the branching occurs during development and how signaling pathways translate into physical changes. While many signaling pathways and structural components have been identified that play a role in branching, they so far have not been incorporated into a comprehensive integrated model that explains branching morphogenesis. This highly dynamic structural process can hardly be understood using conventional molecular biology methods alone. Only a close association between experiments and mathematical modeling will allow an integrated, systems level understanding of the process of branching morphogenesis. We previously generated a simulation framework to model lung branching based on localized proliferation. This model is limited since basement membrane dynamics are critical for branching. Our hypothesis is that basement membrane dynamics controlled by Rho kinase (ROCK)-mediated signaling is a critical component of salivary gland branching morphogenesis. To address this hypothesis and to create a framework for understanding the role of basement membrane dynamics during branching morphogenesis, we propose five specific aims: Specific Aim 1 Develop a simulation framework for salivary gland branching morphogenesis based on Level Set Methods, Specific Aim 2 Develop the experimental model system and compare experimental results with predictions of the new mathematical model and simulation framework, Specific Aim 3 Investigate the function of cytoskeletal inhibitors on branching morphogenesis and use this data to train the model, Specific Aim 4 Determine if ROCK inhibitors affect cytoskeletal tension during branching morphogenesis, and Specific Aim 5 Identify the cellular mechanism by which ROCK affects branching morphogenesis. The robust simulation framework and the mathematical models developed as a result of this project will constitute the first crucial step towards development of a comprehensive model of salivary gland branching morphogenesis. Significantly, it will guide experimentalists by revealing missing links and suggesting directions for future research. Further, the mathematical model and simulation framework can be modified as more data is obtained and will provide us with a tool to predict, and eventually, control cell behavior on different matrix substrates for intelligent engineering of a functional salivary gland. Project Narrative: The data obtained from this grant will advance basic scientific knowledge regarding the role of the basement membrane in control of branching morphogenesis in the salivary gland. In addition, we will create a mathematical model that incorporates experimental analysis of both biochemical and physical control. The model will be implemented in an appropriate numerical framework. This software tool will be accessible by a front end user-friendly interface, such that it will be available for other experimental biologists to use as a research tool for testing hypothesis in silico before experimenting with live tissue. Finally, in generating this model that allows us to describe and predict cell behavior within this context, we will gain insights into new methods for controlling cells for engineering of tissues which require prediction of cell behavior. This work will lead to generation of new models for tissue engineering.